A technique is proposed as a method of generating a high resolution output image from a low resolution input image, in which pairs of low resolution images and high resolution images of a multiplicity of image contents are studied in advance, a conversion (projection) relationship from low resolution information to high resolution information is obtained, and the projection relationship is used to generate (restore) an image including the high resolution information from the low resolution input image (Non-Patent Literature 1).
The conventional method can be divided into a studying step and a restoration step, and, in the former studying step, the projection relationship between the low resolution information and the high resolution information of the pair group (will be called a “studying image set”) of the low resolution images and the high resolution images are studied in advance using a principal component analysis (PCA) or a tensor singular value decomposition (TSVD). For example, a tensor is obtained, the tensor indicating the projection relationship between modality eigenspaces, such as conversion from a real space of low resolution pixels to a pixel eigenspace and conversion to a personal difference eigenspace (eigenspace) of person, as well as conversion to a high resolution pixel eigenspace and conversion from the high resolution pixel eigenspace to the real space.
Meanwhile, in the restoration step, the studied tensor is used to project input images of arbitrary low resolution information including the studying image set onto images of high resolution information.
According to the technique, the number of variations of modalities (such as individual difference between people, expression of face, resolution of image, face direction, illumination change, and race) of projection conversion can be expressed by the order of the tensor (studying model can be designed accordingly), and the projection that satisfies input conditions allows highly accurate restoration.
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{Non-Patent Literature}
    {NPL 1}    JIA Kui, GONG Shaogang “Generalized Face Super-Resolution”, IEEE Transactions of Image Processing, Vol. 17, No. 6, June 2008 Page. 873-886 (2008).